Proceedings of the 3rd IKDD Conference on Data Science, 2016最新文献_第2页

On the Dynamics of Username Changing Behavior on Twitter 关于Twitter用户名改变行为的动态

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888452

Paridhi Jain, P. Kumaraguru

{"title":"On the Dynamics of Username Changing Behavior on Twitter","authors":"Paridhi Jain, P. Kumaraguru","doi":"10.1145/2888451.2888452","DOIUrl":"https://doi.org/10.1145/2888451.2888452","url":null,"abstract":"People extensively use username to lookup users, their profiles and tweets that mention them via Twitter search engine. Often, the searched username is outdated due to a recent username change and no longer refers to the user of interest. Search by the user's old username results in a failed attempt to reach the user's profile, thereby making others falsely believe that the user account has been deactivated. Such search can also redirect to a different user who later picks the old username, thereby reaching to a different person altogether. Past studies show that a substantial section of Twitter users change their username over time. We also observe similar trends when tracked 8.7 million users on Twitter for a duration of two months. To this point, little is known about how and why do these users undergo changes to their username, given the consequences of unreachability. To answer this, we analyze username changing behavior of carefully selected users on Twitter and find that users change username frequently within short time intervals (a day) and choose new username un-related to the old one. Few favor a username by repeatedly choosing it multiple times. We explore few of the many reasons that may have caused username changes. We believe that studying username changing behavior can help correctly find the user of interest in addition to learning username creation strategies and uncovering plausible malicious intentions for the username change.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114157842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Learning DTW-Shapelets for Time-Series Classification 学习DTW-Shapelets用于时间序列分类

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888456

Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, L. Schmidt-Thieme

{"title":"Learning DTW-Shapelets for Time-Series Classification","authors":"Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, L. Schmidt-Thieme","doi":"10.1145/2888451.2888456","DOIUrl":"https://doi.org/10.1145/2888451.2888456","url":null,"abstract":"Shapelets are discriminative patterns in time series, that best predict the target variable when their distances to the respective time series are used as features for a classifier. Since the shapelet is simply any time series of some length less than or equal to the length of the shortest time series in our data set, there is an enormous amount of possible shapelets present in the data. Initially, shapelets were found by extracting numerous candidates and evaluating them for their prediction quality. Then, Grabocka et al. [2] proposed a novel approach of learning time series shapelets called LTS. A new mathematical formalization of the task via a classification objective function was proposed and a tailored stochastic gradient learning was applied. It enabled learning near-to-optimal shapelets without the overhead of trying out lots of candidates. The Euclidean distance measure was used as distance metric in the proposed approach. As a limitation, it is not able to learn a single shapelet, that can be representative of different subsequences of time series, which are just warped along time axis. To consider these cases, we propose to use Dynamic Time Warping (DTW) as a distance measure in the framework of LTS. The proposed approach was evaluated on 11 real world data sets from the UCR repository and a synthetic data set created by ourselves. The experimental results show that the proposed approach outperforms the existing methods on these data sets.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123187974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 43

Competing Algorithm Detection from Research Papers 研究论文中的竞争算法检测

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888473

S. Ganguly, Vikram Pudi

引用次数: 5

Using Sort-Union to Enhance Economically-Efficient Sentiment Stream Analysis 利用排序联合增强经济高效的情感流分析

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888468

Prateek Goel, Manajit Chakraborty, C. R. Chowdary

引用次数: 0

Audience Prism: Segmentation and Early Classification of Visitors Based on Reading Interests 受众棱镜:基于阅读兴趣的受众细分与早期分类

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888459

Lilly Kumari, Sunny Dhamnani, Akshat Bhatnagar, Atanu R. Sinha, R. Sinha

{"title":"Audience Prism: Segmentation and Early Classification of Visitors Based on Reading Interests","authors":"Lilly Kumari, Sunny Dhamnani, Akshat Bhatnagar, Atanu R. Sinha, R. Sinha","doi":"10.1145/2888451.2888459","DOIUrl":"https://doi.org/10.1145/2888451.2888459","url":null,"abstract":"The largest Media and Entertainment (M&E) web portals today cater to more than 100 Million unique visitors every month. In Customer Relationship Management, customer segmentation plays an important role, with the goal of targeting different products for different segments. Marketers segment their customers based on customer attributes. In the non-subscription based media business, the customer is analogous to the visitor, the product to the content, and a purchase to consumption. Knowing which segment an audience member belongs to, enables better engagement. In this work, we address the problems: 1) How can we segment audience members of an M&E web property based on their media consumption interests? 2) When a new visitor arrives, how can we classify them into one of the above defined segments (without having to wait for consumption history)? We apply our proposed solution to a real world data-set and show that we can achieve coherent clusters and can predict cluster membership with a high level of accuracy. We also build a tool that the editors can find valuable towards understanding their audience.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124615212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Smart filters for social retrieval 用于社会检索的智能过滤器

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888457

Balaji Vasan Srinivasan, Tanya Goyal, N. M. Nainani, Kartik K. Sreenivasan

引用次数: 1

An Approach to Allocate Advertisement Slots for Banner Advertising 一种条幅广告的广告位分配方法

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888472

V. Kavya, P. Reddy

引用次数: 2

Consensus Clustering Approach for Discovering Overlapping Nodes in Social Networks 社会网络重叠节点发现的共识聚类方法

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888471

D. Shankar, S. Bhavani

引用次数: 1

Feature Creation based Slicing for Privacy Preserving Data Mining 基于特征创建的隐私保护数据挖掘切片

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888462

R. Priyadarsini, M. Valarmathi, S. Sivakumari

{"title":"Feature Creation based Slicing for Privacy Preserving Data Mining","authors":"R. Priyadarsini, M. Valarmathi, S. Sivakumari","doi":"10.1145/2888451.2888462","DOIUrl":"https://doi.org/10.1145/2888451.2888462","url":null,"abstract":"In the digital era vast amount of data are collected and shared for purpose of research and analysis. These data contain sensitive information about the people and organizations which needs to be protected during the process of data mining. This work proposes Feature Creation Based Slicing [FCBS] algorithm for preserving privacy such that sensitive data are not exposed during the process of data mining in Multi Trust Level [MTL] environment. The proposed algorithm applies three layers of privacy preservation using both perturbation and non-perturbation techniques and creates new features from already existing attribute vector. Experiments are performed on real life and benchmarked datasets and the results are compared with the existing slicing and L-diversity algorithms. The results show that privacy preserved datasets generated using the proposed algorithm yields negligible hiding failure while protecting sensitive patterns during association mining and gives comparable utility during classification. Due to feature creation process in the proposed algorithm, linking and known background attacks are prevented. Also, the variance values of the proposed privacy preserved datasets show that they can prevent diversity attacks.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134623794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Proceedings of the 3rd IKDD Conference on Data Science, 2016 第三届IKDD数据科学会议论文集，2016

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451

M. Marathe, M. Mohania, Prateek Jain

引用次数: 0